Elitist Guided Parameter Adaptive Brain Storm Optimization Algorithm
Fuqing Zhao, Xiaotong Hu, Jinlong Zhao, Huan Liu · 2021
With the increasing complexity of continuous optimization problems, the requirement of solving algorithms is higher and higher. To improve the performance of brain storm optimization algorithm, an elitist guided parameter adaptive BSO (EGBSO) is proposed in this paper. The population is sorted in the objective space based on the fitness. The top M individuals are regarded as elitists to guide the ordinary individuals to cluster, which accelerates the convergence speed of the algorithm. The updating mechanism of elite guidance is introduced, which utilizes the cooperation between global optimal individual and elitists to guide the population to a better direction. An adaptive selection parameter is set to make the algorithm more inclined to global search in the early stage and local search in the later stage, balancing the exploration and exploitation capabilities. The proposed EGBSO algorithm and three comparison algorithms are tested on the CEC2017 benchmark test suit, and the experimental results show that the EGBSO has good performance in solving complex optimization problems.